Object detectors can be divided into two groups: two-stage detectors, with the Faster R-CNN [1]} being the most representative, and one-stage detectors, such as YOLO [2]}, SSD [3]} and CornerNet [4]}.
Two-stage detectors consist of a region proposal stage, where a sparse set of regions of interest (ROI) is selected, followed by a feature extraction stage for the subsequent classification of each candidate ROI. One-stage methods do not perform ROI pooling and instead use a single network to detect objects. They are faster than two-stage detectors, but have lower localization accuracy.
We use our saliency technique to analyze both two-stage and single-stage detectors (Faster R-CNN and YOLO, respectively).
